Demand Management Research Articles

Development of an integrated urban modelling framework for examining the impacts of work from home on travel behavior

This paper develops an integrated urban modelling framework (IUMF) to predict how work from home (WFH) decision affects travel behavior. First, it conducts a questionnaire survey among working professionals in Halifax, Canada, to collect data on their socio-demographic characteristics, mode choice, vehicle ownership, and work-arrangement. Bayesian Belief network models are developed using the collected responses to calculate the cumulative probability tables (CPTs) of variables associated with the decision to WFH. Next, the ascertained CPTs are used as input to extend an integrated urban modelling framework (IUMF) that is further utilized to simulate individuals’ work from home choices and travel behavior up to 2025 for Halifax, Canada. Results indicate that around 57% of the workers would like to WFH and 7% wants to relocate closer to workplace. The model forecasts a significant preference for remote work among individuals with offices in the urban core. Results also show that auto mode share is increased to 79% in 2024, whereas transit, walking and biking trips decreased. Average travel distance is higher in the post-pandemic compared to the pre-pandemic, while travel distance of telecommuters is found to be higher than non-telecommuters. Statistically significant differences are observed between telecommuters and non-telecommuters for ‘number of activities’ and ‘distance travelled’ in a day. The outcomes of this study will offer policy makers a better understanding of long-term impacts of WFH on transport and land-use systems and help to develop effective travel demand management strategies.

Enhancing knowledge sharing platforms with machine learning

In the contemporary digital landscape, knowledge sharing platforms serve as crucial tools for facilitating collaboration and information exchange among individuals and organizations. However, ensuring the relevance and effectiveness of shared knowledge remains a persistent challenge. This abstract explores the application of machine learning techniques to enhance knowledge sharing platforms. By leveraging machine learning algorithms and predictive analytics, these platforms can better anticipate users' information needs, personalize content recommendations, and improve overall user experience. Through a comprehensive analysis of existing knowledge sharing platforms and the implementation of machine learning models, this abstract highlights the potential benefits of integrating machine learning techniques into knowledge sharing platforms. The findings underscore the significance of personalized content delivery, improved information discovery, and enhanced collaboration facilitated by machine learning-driven knowledge sharing platforms. Conclusion: Ultimately, the integration of machine learning techniques represents a promising approach to address the evolving demands of knowledge management and foster more efficient and effective knowledge sharing processes in various domains.

Analysis on the Social Demand and Training Path of the Aged Service Talents: Take Dazhou City of Sichuan Province as An Example

The training of senior service personnel is an important part of the development of senior service industry. The lack of senior service personnel seriously restricts the improvement of senior service quality. At present, there is a large gap in the demand for talents in the elderly care service, the talent structure is unreasonable, and the talent training mechanism is not perfect. In order to better promote the development of pension services, it is necessary to make plans for the construction of pension service talent team as soon as possible, increase preferential policies for the training of elderly service and nursing talents, and vigorously support local colleges and universities to apply for pension service management, nursing and rehabilitation therapy and other related majors, so as to realize the healthy and sustainable development of pension services. This report aims to investigate how can Adidas survive and improve in a consistently changing environment and strong competition. This report focuses on the multi-billion dollar shoe industry in 2021, with green, sustainable, and innovative themes. Sports and fashion will become the trend of the future industry. Adidas' global revenue is 21.23 billion euros, lagging behind the powerful Nike, with a growth rate of 16% compared to 2020, but still lagging behind the level before the 2019 pandemic. A turbulent environment, capturing consumer demand and supply chain management are the main risk issues. The company has made progress in climate change and SDG's, establishing a good brand image, but only 42% of supplier facilities met the standards and lacked the comprehensiveness of goals. They are committed to commercial product innovation, but there is a lack of internal innovation and innovative product sustainability. They opt for a detailed and systematic approach to supply chain management. They also pay attention to employee development and a fair environment, but lack concern for daily life. Adidas has a sound risk management system and has effectively responded to epidemic risks, but measures to address small and potential risks need to be improved. Because they have a certain business scale and continue to work hard, Adidas can survive in the constantly changing environment and competition, but surpassing Nike may require breakthroughs.

Artificial Intelligence in Banking Internal Demand Management Systems: The Example of Vakıf Participation Bank

The development of artificial intelligence and technology has accelerated the transformation of internal processes in the banking sector. In particular, Natural Language Processing (NLP) technology provides time and cost savings by automating processes such as data entry, querying, and reporting. While NLP-based systems increase customer satisfaction by understanding customer demands and providing appropriate responses quickly, they also increase operational efficiency. Classification algorithms, which are frequently used together with NLP technology, analyze text data and assign them to certain categories or classes, creating a powerful combination for the processing and analysis of text-based data. Vakıf Participation Demand Management System R&D Project has developed an NLP and classification model to be used in its internal processes. With the developed model, it was aimed to eliminate the problems encountered in workflow processes and increase efficiency by developing a language understanding model using the records of requests (demand management system) kept within Vakıf Participation and frequently used in operational processes. During this study, existing data containing in-house requests were subjected to pre-processing, and model training studies were carried out with these data. As a result of the developments, a model with 75% accuracy was developed and improvement efforts on the model continue. Thanks to the developed model, aims to shorten the response time for requests in the demand management system, reduce operational burdens, and increase internal customer satisfaction. It is planned to use the developed model in other banking internal processes as well.

An overview of dynamic pricing toll roads in the United States: Pricing algorithms, operation strategies, equity concerns, and funding mechanism

Toll roads with fully dynamic pricing schemes could play a key role in addressing the economic and environmental tolls imposed by increasing congestion levels in major metropolitan areas. Transportation professionals usually argue that they could provide additional revenue streams to accelerate the delivery of much-needed highway projects and serve as a tool for travel demand management. However, the number of dynamic pricing toll roads only increased recently in the U.S., and their performance has not been empirically evaluated systematically. This study provides a comprehensive review of U.S. dynamic pricing toll roads. Pricing is reactive to demand, and additional travel demand management strategies such as incentives for high occupancy vehicles, clean vehicles, and transit are commonly included. Both conventional project delivery methods and private–public partnerships have been used for such projects. Financial statements from a few P3 dynamic toll road projects suggest they are taking full advantage of TIFIA loans and are performing well during their short history. Equity issues are commonly raised, and many projects address them specifically through subsidies to vulnerable groups or re-investment in the community. Further quantitative analyses are needed to test if dynamic toll roads are an efficient tool to address the financial, economic, and environmental challenges major metropolitan areas face.

Applications of generative AI-based financial robot advisors as investment consultants

As a new wave of technological and industrial revolution accelerates, the fusion of technological evolution and societal development is reshaping industries, with digitalization emerging as a potent force in reshaping competitive landscapes. Artificial Intelligence (AI) has progressively permeated various sectors, including finance, its application in finance holds the promise of enhancing efficiency and service quality for financial institutions Its application in finance holds the promise of enhancing efficiency and service quality for financial institutions, thereby offering improved experiences for clients. Among these applications, Intelligent Robo-Advisors stand out as a particularly notable field. In recent years, the rapid development of financial literacy and risk awareness among Chinese residents has elevated the complexity of wealth management demands, prompting interest in Intelligent Robo-Advisors as providers of professional wealth management services. This paper aims to explore the application of Artificial Intelligence in securities investment, focusing on Generative AI-based Financial Robot Advisors as Investment Consultants.

The safety and environmental impacts of battery storage systems in renewable energy

The integration of battery storage systems in renewable energy infrastructure has garnered significant attention due to its potential to enhance energy reliability, efficiency, and sustainability. However, alongside these benefits, concerns persist regarding the safety and environmental impacts associated with the deployment and operation of such systems. This review explores the multifaceted aspects of safety and environmental considerations in battery storage systems within the context of renewable energy. Firstly, safety concerns encompass a range of factors, including thermal runaway, fire hazards, and chemical leakage, which pose risks to both human life and property. Mitigation strategies such as advanced battery management systems and fire suppression technologies are critical for addressing these risks effectively. Secondly, environmental impacts arise throughout the lifecycle of battery storage systems, from raw material extraction to end-of-life disposal. Key issues include resource depletion, greenhouse gas emissions, and pollution from mining activities. Sustainable practices such as responsible sourcing of materials, recycling initiatives, and the development of second-life applications are essential for minimizing environmental footprints. Furthermore, the interaction between battery storage systems and renewable energy sources introduces complexities in assessing environmental impacts. While battery storage facilitates the integration of intermittent renewables like solar and wind by providing grid stabilization and energy storage capabilities, its environmental benefits may be compromised by factors such as energy-intensive manufacturing processes and reliance on non-renewable resources. The safety and environmental impacts of battery storage systems in renewable energy demand comprehensive evaluation and management strategies to maximize benefits while minimizing risks. Collaboration among stakeholders, technological innovation, and regulatory frameworks are crucial for promoting the sustainable deployment and operation of these systems in the transition towards a cleaner and more resilient energy future.

Exploring the Efficacy of Random Linear Parameter Models for Forecasting Heating Demand in District Heating Networks

The heating and cooling sector accounts for approximately half of global energy consumption, making it a pivotal focus for energy transition efforts. District heating and cooling networks have emerged as efficient, innovative, and dominant technologies in various regions. Accurate forecasting of heating demand within these networks is imperative for effective energy transition and responsive demand management. While existing forecasting techniques primarily concentrate on limited parameters such as ambient temperature and time of day, this paper explores the multifaceted impact of various factors, including ambient temperature, energy prices, number of consumers, building types, and solar radiation, on heating demand. Through a comprehensive study of these determinants, a robust and efficient forecasting model is developed in this research. Specifically, a novel Random Linear Parameter Model (RLPM) is introduced for hourly heating demand forecasting within district heating networks. The model's performance is assessed and compared with existing forecasting models, namely Linear Regression, ARIMA, and RLPM. A notable advantage of the Random Parameter Linear Regression model lies in its capability to recognize unobserved parameters influencing demand and account for unobserved heterogeneity, providing a robust framework for incorporating these factors into the forecasting process. This study aims to underscore the proposed approach's advantages in terms of accuracy and its ability to estimate the determinants of heating load, thereby offering reliable future forecasting. The findings have the potential to significantly contribute to optimizing district heating systems by delivering more nuanced and dependable predictions of heating demand.

Website posts and the voice offrontline healthcare workers: athematic analysis.

With the soaring rise in popularity of social media platforms in recent decades, the use of website posts for the expression of work-related views has also increased. Despite websites being extensively used, there has been no examination of the views and concerns expressed by frontline workers through website posts. The present research aims to contribute to the "voice literature" first by evaluating how frontline workers utilize anonymous media platforms to express their views and work-related concerns and, second, by demonstrating how anonymous voice systems can encourage frontline health workers in providing feedback and dissatisfaction. The study utilizes the thematic analysis method to analyze the content of posts by psychologists on a collaborative consultation website administrated by Israel's Ministry of Health, discussing their perceptions of work-related concerns. The analysis identified three work-related themes through the employees' voices. These include insufficient support from management, conflicts and excessive occupational demands. The workers expressed their apprehension with regard to organizational pressures, deficient budgetallocations, excessive workloads, lack of recognition and work-life imbalances. The application of thematic analysis method to anonymous open-public data should be viewed as an effective, affordable, genuine and unique research method for data analysis. Anonymous platforms can generate unique insights that may not be possible through traditional means. This can provide practitioners with a comprehensive understanding of various issues and challenges and be a useful tool for identifying shortcomings within health settings.

Reframing water demand management: a new co-governance framework coupling supply-side and demand-side solutions toward sustainability

Abstract. Water demand management adopts economic and non-economic measures to reduce human water use. However, it is argued in this study that water use changes may cause idle water supply facilities and revenue losses, thereby challenging the sustainability of water supply systems in the context of climate change. A co-governance framework was established to inspire practical strategies of sustaining water supply systems by re-evaluating the long-term impacts of water demand changes. This framework adopts a broader view of water demand management by integrating the political, financial, and consumptive needs of the government, the market, and the users in the form of a collaborative strategy coupling both supply-side and demand-side solutions. The proposed framework was applied to the analysis of the sustainability of China's South-to-North Water Diversion Project. It is found that the South-to-North Water Diversion Project is not a simple water supply infrastructure but rather a synthesis of supply-side and demand-side water management solutions. Actively releasing water for ecological and cultural purposes is suggested in this study to maintain the socio-ecological benefits of the project in the context of human water use decline. The economic cost of the water supply could be recovered by ongoing revenues that include not only the water fees charged to users but also the benefits gained from cooperative investment in broader water-related businesses by both the state-owned water transfer company and local governments in water-receiving areas. The proposed framework and strategies are valuable for other water utilities around the world, especially those challenged by reduced water demand caused by climate change, high water prices, and economic depression.

A multi-objective optimization-based framework for extending reservoir service life in a changing world

Reservoirs are an essential component of water resources systems, improving water supply reliability and security. With long-term climate and water demand changes, the performance of reservoirs may deteriorate, necessitating interventions such as infrastructure upgrades and/or water demand management. These interventions often require substantial financial investment, have long implementation time or impede economic development. Therefore, a more cost-effective and less disruptive alternative is needed to extend the service life of existing reservoir systems. Adapting reservoir operation policies to cater for future changes is such an alternative. By adapting reservoir operation policies to future water availability and demand changes, the capacity of existing systems can be fully utilized to avoid or postpone more expensive and disruptive interventions. However, there is a lack of an integrated framework for estimating the potential of service life extension by re-optimizing operation policies to cater to uncertain changes in future conditions. In this study, we develop a multi-objective optimization-based framework to allow operation policies to adapt to uncertain future changes in water availability and demand by optimizing operation policies for a range of plausible future conditions, thus enabling the service life of existing reservoir systems to be maximized albeit uncertainty in future changes. The developed framework is demonstrated using a reservoir system in Northern Australia. Results show that the service life of the reservoir can be extended by up to 40 years considering uncertain future water availability and demand. In addition, significant changes in operation policies and system responses with time are identified, providing valuable information for developing long-term reservoir planning strategies.

Time Management Practices: Its Impact on Student Athletes' Performance

This study determined the time management practices and the performance of student-athletes. Focusing on key aspects such as student-athlete profiles, the level of time management practices, the impact of these practices, factors influencing student-athletes, and the significant relationship between profiles and time management levels, this research utilized a quantitative descriptive design. Employing a purposive random sampling method, the study encompassed 200 student-athlete respondents from the six prestigious GensanPRISAA colleges in General Santos City. Results showed that respondents were predominantly male, aged 21-23, engaged in basketball and team sports, and enrolled as first-year Criminology students with notable academic performance and local competition achievements. Results revealed a high level of time management skills demonstrated by student-athletes in managing both training sessions and academic schedules. These skills significantly influenced academic performance, athletic achievements, social relationships, and personal well-being. Additionally, the study identified various factors affecting time management, including school and teacher demands, athletic commitments, personal priorities, and social relationships. Surprisingly, there was no significant relationship between demographic profiles and time management practices, indicating other influential factors. To address these findings, the study suggested Enhancing the Student-Athlete Development Program to comprehensively support student-athletes in improving time management skills across different domains. In conclusion, the study emphasized the importance of time management skills among student-athletes and advocated for tailored support programs to foster their holistic development, regardless of demographic characteristics.

The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy.

Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score®, MammaPrint, Prosigna®, EndoPredict®, and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict® have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.

Anomalous Correlation between Thermal Conductivity and Elastic Modulus in Two-Dimensional Hybrid Metal Halide Perovskites.

Device-level implementation of soft materials for energy conversion and thermal management demands a comprehensive understanding of their thermal conductivity and elastic modulus to mitigate thermo-mechanical challenges and ensure long-term stability. Thermal conductivity and elastic modulus are usually positively correlated in soft materials, such as amorphous macromolecules, which poses a challenge to discover materials that are either soft and thermally conductive or hard and thermally insulative. Here, we show anomalous correlations of thermal conductivity and elastic modulus in two-dimensional (2D) hybrid organic-inorganic perovskites (HOIP) by engineering the molecular interactions between organic cations. By replacing conventional alkyl-alkyl and aryl-aryl type organic interactions with mixed alkyl-aryl interactions, we observe an enhancement in elastic modulus with a reduction in thermal conductivity. This anomalous dependence provides a route to engineer thermal conductivity and elastic modulus independently and a guideline to search for better thermal management materials. Further, introducing chirality into the organic cation induces a molecular packing that leads to the same thermal conductivity and elastic modulus regardless of the composition across all half-chiral 2D HOIPs. This finding provides substantial leeway for further investigations in chiral 2D HOIPs to tune optoelectronic properties without compromising thermal and mechanical stability.

Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility.

Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.

Evaluating four decades of energy policy evolution for sustainable development of a South Asian country—Nepal: A comprehensive review

AbstractIn this study, we assessed the accomplishments and shortcomings of an exhaustive collection of energy policies of Nepal over four decades, using a five‐dimensional energy security framework (availability, affordability, technology, sustainability and governance) for sustainable development. We adopted a mixed‐method approach involving thorough review of 70 policy documents (1984–2022), systematic review of 86 peer‐reviewed journal articles on Nepal's energy policy, and consultations with 11 experts. Our evaluation shows that while there is a progressive trend, Nepal's energy policies face challenges of political instability, governance issues, siloed development practices, lagging research and development, inefficient energy demand management, and heavy reliance on international support. Additionally, we offer four tailored recommendations for the related stakeholders: supply‐side management, demand‐side management, multi‐sector collaboration, and political stability and good governance. The insights and recommendations we provide have significant regional implications, particularly in the context of potential cross‐border clean electricity sharing in South Asia.

Online EVs Vehicle-to-Grid Scheduling Coordinated with Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach

The integration of electric vehicles (EVs) into vehicle-to-grid (V2G) scheduling offers a promising opportunity to enhance the profitability of multi-energy microgrid operators (MMOs). MMOs aim to maximize their total profits by coordinating V2G scheduling and multi-energy flexible loads of end-users while adhering to operational constraints. However, scheduling V2G strategies online poses challenges due to uncertainties such as electricity prices and EV arrival/departure patterns. To address this, we propose an online V2G scheduling framework based on deep reinforcement learning (DRL) to optimize EV battery utilization in microgrids with different energy sources. Firstly, our approach proposes an online scheduling model that integrates the management of V2G and multi-energy flexible demands, modeled as a Markov Decision Process (MDP) with an unknown transition. Secondly, a DRL-based Soft Actor-Critic (SAC) algorithm is utilized to efficiently train neural networks and dynamically schedule EV charging and discharging activities in response to real-time grid conditions and energy demand patterns. Extensive simulations are conducted in case studies to testify to the effectiveness of our proposed approach. The overall results validate the efficacy of the DRL-based online V2G scheduling framework, highlighting its potential to drive profitability and sustainability in multi-energy microgrid operations.

Optimal energy scheduling for residential consumers with demand management strategies

ABSTRACT The enhancement of energy networks and cost reduction can be achieved through the implementation of solutions like optimal energy scheduling for residential consumers. This paper proposes day-ahead electrical and thermal energy scheduling for residential sections in energy networks as a bi-criteria optimization approach. The bi-criteria optimization approach is modeled to minimize the operation cost and maximize the reliability of the energy system. Demand management strategies such as demand reduction and demand shifting are considered for residential consumers. The participation of residential consumers in demand management is presented considering electricity prices in the energy markets. The solving bi-criteria optimization approach is done via the weight sum method and LINMAP procedure-based-fuzzy approach. The proposed modeling is implemented in GAMS optimization software for verifying the demand management strategies in optimal energy scheduling in two case studies.

An Avatar's worth in the metaverse workplace: Assessing predictors of avatar customization valuation

Virtual economies with 3D assets have been studied for decades, often in the context of entertainment, but the concept of the metaverse as a workplace platform has only recently begun to take hold. This research addresses a gap in our understanding of how the enhanced ability for impression management in the metaverse workplace may relate to worker well-being and equity. We explore how demographics and previous virtual meeting (VM) experiences relate to people's valuation of self-presentation in the metaverse, reflected by willingness to pay for avatar customizations in a work context. Survey responses from a general population of adults (n = 553) suggest that the valuation of avatar customization in the metaverse workplace was predicted by VM fatigue, gamers' propensity to purchase virtual items, and demographics. People of color and women exhibited higher intentions to purchase avatar customization in the metaverse workplace. These results support the reasoning that the demand for impression management in the metaverse workplace will likely motivate avatar customization, and that gamers are likely to be the early adopters of paid avatar customization options in the non-game metaverse. This study contributes to an understanding of the role avatar customization has for a self-presentation strategy to meet norms in the future metaverse workplace. We discuss implications of VM fatigue and self-presentation concerns from the perspective of an equitable avatar-mediated workplace.

A thermal management design using phase change material in embedded finned shells for lithium-ion batteries

Phase change material (PCM) has recently been regarded as a promising thermal management means for lithium-ion (Li-ion) batteries. However, solid-liquid phase change of many current PCMs occurs in a narrow temperature range, and the cooling performance of their liquid phase is rather poor. To tackle these issues, this article presents a battery thermal management (BTM) design which injects PCM into an aluminum shell around each cell, and machines the shell inner surfaces into a row of straight rectangular fins immersed into the PCM and its outer surfaces into a plain-fin shape exposed in the air. In the mild thermal conditions, the finned shells of two adjacent cells can be embedded and passive PCM cooling is employed. Once the PCM completely melts, the shells will move apart and an airflow will be driven through the gap between them. The BTM design can also protect Li-ion batteries in a freezing environment, by taking advantage of PCM solidification when the finned shells return to their embedded configuration. A series of experiments were conducted to examine its effectiveness on two 50Ah lithium nickel cobalt manganese oxide (NCM) prismatic batteries, which were discharged at a capacity-rate of 2C and at room temperatures of T0=25∘C, 40∘C and −10∘C. It is shown that in the battery discharge at T0=25∘C, PCM in the embedded finned shells effectively reduced the average battery-surface temperature (T¯) and the maximum temperature difference (ΔTmax) by 21% and 36.7%, respectively, compared to bare batteries. At T0=40∘C, this BTM design successfully limited the growth of T¯ to 9.0∘C and led to ΔTmax no more than 1.5∘C. As to the low-temperature scenario, it maintained the batteries at temperatures above T0 for 55.2% longer than those without any BTM protections. Its cooling performance was also compared with that of the conventional liquid-cooling scheme using a bottom serpentine-channel cooling plate. All these results clearly demonstrate that the BTM design in this article can compensate for the deficiencies of conventional PCM-based BTM schemes. Regardless of whether the PCM phase change is available, it has well coped with different thermal management demands of Li-ion batteries.